ABSTRACT Trans-ancestry genetic analysis can facilitate the discovery of trait- or disease-associated loci, characterize shared and differential genetic architectures across populations, improve the delineation of causal variants, and is critical for equal delivery of genomic knowledge and precision healthcare globally. However, current trans-ancestry genetic research is impeded by (i) limited genomic resources for non-European populations; and (ii) limited statistical methods that can appropriately model and integrate data from diverse populations. This project will address these challenges by (i) aggregating and harmonizing genetic data, physical measures, laboratory tests and disease information from global biobanks and multiple health care systems in the United States, with >795K samples of non-European ancestry and a total sample size >1.5M by 2023; and (ii) developing statistical methods and improving practices to integrate multi-ancestry data for cross-population characterization of genetic architectures, meta-analysis, statistical fine-mapping and polygenic prediction. Specifically, in Aim 1, we will systematically characterize the genetic underpinnings of human complex traits and common diseases at variant, locus, regional and genome-wide levels across diverse populations, and discover and validate novel genetic loci through trans-ancestry meta-analysis. In Aim 2, we will develop scalable, robust, accurate and flexible statistical methods for trans-ancestry fine-mapping, delineate putative causal genetic variants for a range of complex traits and diseases, and explore their functional consequences and biological mechanisms. In Aim 3, we will develop haplotype-based methods for improved trans-ancestry polygenic prediction, and benchmark the clinical utility of polygenic scores in disease risk prediction across diverse populations. Leveraging large-scale biobank resources and novel simulation frameworks, we will additionally enable fair and rigorous comparisons of existing and emerging methods for the integrative analysis of multi-ancestry data, and assess various analysis choices and practical considerations in trans-ancestry fine- mapping and genetic prediction in order to inform future study design and analysis plan, as well as methods development, evaluation and application in trans-ancestry settings.

PROJECT NARRATIVE Trans-ancestry genetic analysis can facilitate genomic discoveries and is critical for equal delivery of precision healthcare globally. However, current trans-ancestry genetic research is impeded by limited genomic resources for non-European populations and inadequate statistical methods that can appropriately model and analyze data from ancestrally diverse populations. This project will address these challenges by aggregating and harmonizing data from large-scale biobanks across the globe, and developing novel statistical methods that can integrate data from multiple populations for improved trans-ancestry genetic studies.